A digital twin-based decision support approach for AGV scheduling

The dynamic and complex environment affects the operational efficiency of automated guided vehicles (AGVs) at automated container terminals, in which artificial intelligent approaches are applied to address optimization problems in dynamic systems. This paper proposes a digital twin-based decision s...

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Main Authors: Gao, Yinping, Chang, Daofang, Chen, Chun-Hsien, Sha, Mei
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2024
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Online Access:https://hdl.handle.net/10356/176062
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1760622024-05-13T04:45:03Z A digital twin-based decision support approach for AGV scheduling Gao, Yinping Chang, Daofang Chen, Chun-Hsien Sha, Mei School of Mechanical and Aerospace Engineering Engineering Digital twin Decision making The dynamic and complex environment affects the operational efficiency of automated guided vehicles (AGVs) at automated container terminals, in which artificial intelligent approaches are applied to address optimization problems in dynamic systems. This paper proposes a digital twin-based decision support approach to improve the efficiency of AGV scheduling service. Accordingly, the factors that affect AGV scheduling performance, including conflicts, failures, and battery constraints, are discussed first. Then, the framework and main steps of the proposed approach are described. The physical operation space is mapped into the virtual space, and both spaces keep synchronized to support the verification of solutions. A mathematical programming model and Q-learning algorithm are used to generate the AGV scheduling plan considering battery charging. Numerical experiments are conducted to demonstrate the effectiveness of the proposed approach. Comparisons of the digital twin-based approach, genetic algorithm (GA), and particle swarm optimization (PSO) are also made with different scale experiments. It appears that the proposed digital twin-based approach is superior to GA and PSO when solving small- and large-scale cases. A sensitivity analysis is performed concerning the battery utilization rate, task, and AGV number. Experimental results show that an optimal configuration of AGV and task can improve the battery utilization rate and reduce the completion time. This work was supported by the National Natural Science Foundation of China Grant (No. 71972128). 2024-05-13T04:45:03Z 2024-05-13T04:45:03Z 2024 Journal Article Gao, Y., Chang, D., Chen, C. & Sha, M. (2024). A digital twin-based decision support approach for AGV scheduling. Engineering Applications of Artificial Intelligence, 130, 107687-. https://dx.doi.org/10.1016/j.engappai.2023.107687 0952-1976 https://hdl.handle.net/10356/176062 10.1016/j.engappai.2023.107687 2-s2.0-85181089106 130 107687 en Engineering Applications of Artificial Intelligence © 2023 Elsevier Ltd. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Digital twin
Decision making
spellingShingle Engineering
Digital twin
Decision making
Gao, Yinping
Chang, Daofang
Chen, Chun-Hsien
Sha, Mei
A digital twin-based decision support approach for AGV scheduling
description The dynamic and complex environment affects the operational efficiency of automated guided vehicles (AGVs) at automated container terminals, in which artificial intelligent approaches are applied to address optimization problems in dynamic systems. This paper proposes a digital twin-based decision support approach to improve the efficiency of AGV scheduling service. Accordingly, the factors that affect AGV scheduling performance, including conflicts, failures, and battery constraints, are discussed first. Then, the framework and main steps of the proposed approach are described. The physical operation space is mapped into the virtual space, and both spaces keep synchronized to support the verification of solutions. A mathematical programming model and Q-learning algorithm are used to generate the AGV scheduling plan considering battery charging. Numerical experiments are conducted to demonstrate the effectiveness of the proposed approach. Comparisons of the digital twin-based approach, genetic algorithm (GA), and particle swarm optimization (PSO) are also made with different scale experiments. It appears that the proposed digital twin-based approach is superior to GA and PSO when solving small- and large-scale cases. A sensitivity analysis is performed concerning the battery utilization rate, task, and AGV number. Experimental results show that an optimal configuration of AGV and task can improve the battery utilization rate and reduce the completion time.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Gao, Yinping
Chang, Daofang
Chen, Chun-Hsien
Sha, Mei
format Article
author Gao, Yinping
Chang, Daofang
Chen, Chun-Hsien
Sha, Mei
author_sort Gao, Yinping
title A digital twin-based decision support approach for AGV scheduling
title_short A digital twin-based decision support approach for AGV scheduling
title_full A digital twin-based decision support approach for AGV scheduling
title_fullStr A digital twin-based decision support approach for AGV scheduling
title_full_unstemmed A digital twin-based decision support approach for AGV scheduling
title_sort digital twin-based decision support approach for agv scheduling
publishDate 2024
url https://hdl.handle.net/10356/176062
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